Tootfinder

Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
Tianyu Chen, Jian Lou, Wenjie Wang
https://arxiv.org/abs/2506.10030 https:/…

Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environment
As Retrieval-Augmented Generation (RAG) evolves into service-oriented platforms (Rag-as-a-Service) with shared knowledge bases, protecting the copyright of contributed data becomes essential. Existing watermarking methods in RAG focus solely on textual knowledge, leaving image knowledge unprotected. In this work, we propose AQUA, the first watermark framework for image knowledge protection in Multimodal RAG systems. AQUA embeds semantic signals into synthetic images using two complementary meth…

Minnesota political assassinations
https://www.nytimes.com/live/2025/06/14/us/minnesota-shootings?smtyp=cur&unlocked_article_code=1.O08.d4NX.fqrqIQDXzcvn&…

Suspect Identified In Minnesota Lawmakers Shootings: Live Updates
The suspect was identified as Vance Boelter, 57, by law enforcement officials with knowledge of the investigation into the killing of a state representative and her husband and the attack on a state senator and his wife. He remains at large.

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new/
[1/4]:
Croppable Knowledge Graph Embedding
https://arxi…

"Conspirituality is a kind of crowd-sourced knowledge, where the spread of a belief is taken to be support for its content."
https://www.independent.co.uk/life-style/wellness-right-wing-conspiracy-conspirituality-b27…

(De)composing Craft: An Elementary Grammar for Sharing Expertise in Craft Workflows
Ritik Batra, Lydia Kim, Ilan Mandel, Amritansh Kwatra, Jane L. E., Steven J. Jackson, Thijs Roumen
https://arxiv.org/abs/2506.10891

(De)composing Craft: An Elementary Grammar for Sharing Expertise in Craft Workflows
Craft practices rely on evolving archives of skill and knowledge, developed through generations of craftspeople experimenting with designs, materials, and techniques. Better documentation of these practices enables the sharing of knowledge and expertise between sites and generations. However, most documentation focuses solely on the linear steps leading to final artifacts, neglecting the tacit knowledge necessary to improvise, or adapt workflows to meet the unique demands of each craft project.…

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
https://arxiv.org/abs/2506.10347

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with S…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new/
[1/3]:
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion

HiKO: A Hierarchical Framework for Beyond-Second-Order KO Codes
Shubham Srivastava, Adrish Banerjee
https://arxiv.org/abs/2506.10121 https://

HiKO: A Hierarchical Framework for Beyond-Second-Order KO Codes
This paper introduces HiKO (Hierarchical Kronecker Operation), a novel framework for training high-rate neural error-correcting codes that enables KO codes to outperform Reed-Muller codes beyond second order. To our knowledge, this is the first attempt to extend KO codes beyond second order. While conventional KO codes show promising results for low-rate regimes ($r < 2$), they degrade at higher rates -- a critical limitation for practical deployment. Our framework incorporates three key innova…

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Eun Som Jeon, Sinjini Mitra, Jisoo Lee, Omik M. Save, Ankita Shukla, Hyunglae Lee, Pavan Turaga
https://arxiv.org/abs/2506.10265

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Human gait analysis with wearable sensors has been widely used in various applications, such as daily life healthcare, rehabilitation, physical therapy, and clinical diagnostics and monitoring. In particular, ground reaction force (GRF) provides critical information about how the body interacts with the ground during locomotion. Although instrumented treadmills have been widely used as the gold standard for measuring GRF during walking, their lack of portability and high cost make them impracti…

Situated Bayes -- Feminist and Pluriversal Perspectives on Bayesian Knowledge
Juni Schindler, Goda Klumbyt\.e, Matthew Fuller
https://arxiv.org/abs/2506.09472

Situated Bayes -- Feminist and Pluriversal Perspectives on Bayesian Knowledge
This is the introduction and lead article to the Situated Bayes special issue of Computational Culture. The article introduces Bayes' Theorem and aspects of its contemporary uses, for instance in machine learning. A mathematical discussion is developed alongside a consideration of Bayes Theorem in relation to critical theories of knowledge, specifically the discussion of situated knowledge in feminist theories of science, pluriversal knowledge in decolonial theory, and critical approaches to ma…

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
Christian Reichenb\"acher, Philipp Rank, Jochen Hipp, Oliver Bringmann
https://arxiv.org/abs/2506.10098

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
This paper presents the first application of Gaussian Mixture Copula Models to the statistical modeling of driving scenarios for the safety validation of automated driving systems. Knowledge of the joint probability distribution of scenario parameters is essential for scenario-based safety assessment, where risk quantification depends on the likelihood of concrete parameter combinations. Gaussian Mixture Copula Models bring together the multimodal expressivity of Gaussian Mixture Models and the…

Structured Graph Representations for Visual Narrative Reasoning: A Hierarchical Framework for Comics
Yi-Chun Chen
https://arxiv.org/abs/2506.10008 https://…

Structured Graph Representations for Visual Narrative Reasoning: A Hierarchical Framework for Comics
This paper presents a hierarchical knowledge graph framework for the structured understanding of visual narratives, focusing on multimodal media such as comics. The proposed method decomposes narrative content into multiple levels, from macro-level story arcs to fine-grained event segments. It represents them through integrated knowledge graphs that capture semantic, spatial, and temporal relationships. At the panel level, we construct multimodal graphs that link visual elements such as charact…

Seeding an Uncertain Technology
Eric Gao
https://arxiv.org/abs/2506.10340 https://arxiv.org/pdf/2506.10340

Seeding an Uncertain Technology
I study how a startup with uncertainty over product quality and no knowledge of the underlying diffusion network optimally chooses initial seeds. To ensure widespread adoption when the product is good while minimizing negative perceptions when it is bad, the optimal number of initial seeds should grow logarithmically with network size. When there are agents of different types that govern their connectivity, it is asymptotically optimal to seed agents of a single type: the type that minimizes th…

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods
Yeonwoo Jang, Shariqah Hossain, Ashwin Sreevatsa, Diogo Cruz
https://arxiv.org/abs/2506.10236

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods
In this work, we show that some machine unlearning methods may fail when subjected to straightforward prompt attacks. We systematically evaluate eight unlearning techniques across three model families, and employ output-based, logit-based, and probe analysis to determine to what extent supposedly unlearned knowledge can be retrieved. While methods like RMU and TAR demonstrate robust unlearning, ELM remains vulnerable to specific prompt attacks (e.g., Hindi filler text in original prompt recover…

Numerical tests of cosmological Alfv\'en waves with Ohmic diffusion
Orestis A. Karapiperis, Matthieu Schaller
https://arxiv.org/abs/2506.10684 https://…

Numerical tests of cosmological Alfvén waves with Ohmic diffusion
Physical problems with a solution that can be expressed analytically are scarce; this holds even more true for problems set in a cosmological context. Such solutions are, however, invaluable tools for making comparisons between theory, numerical experimentation and observations. In this work we present what to our knowledge is the first set of non-trivial closed-form expressions describing the behaviour of a system governed by the equations of non-ideal Magnetohydrodynamics (MHD), where the eff…

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

On the Earth's tidal perturbations. II. LARES 2 satellite
V. G. Gurzadyan, I. Ciufolini, H. G. Khachatryan, S. Mirzoyan, A. Paolozzi
https://arxiv.org/abs/2506.10310

On the Earth's tidal perturbations. II. LARES 2 satellite
Laser-ranging satellites have proved their efficiency in high precision testing of General Relativity and constraining modified gravity theories proposed to explain the dark sector and the cosmological tensions. The LARES 2 satellite launched in 2022, is currently providing improved information regarding the frame-dragging effect predicted by General Relativity, as well as the geodesy, thus essentially complementing the data of already existing laser-ranging satellites. The proper knowledge of …

On the Performance of Cloud-based ARM SVE for Zero-Knowledge Proving Systems
Dumitrel Loghin, Shuang Liang, Shengwei Liu, Xiong Liu, Pingcheng Ruan, Zhigang Ye
https://arxiv.org/abs/2506.09505

On the Performance of Cloud-based ARM SVE for Zero-Knowledge Proving Systems
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations…

Incorporating Linguistic Constraints from External Knowledge Source for Audio-Visual Target Speech Extraction
Wenxuan Wu, Shuai Wang, Xixin Wu, Helen Meng, Haizhou Li
https://arxiv.org/abs/2506.09792

Incorporating Linguistic Constraints from External Knowledge Source for Audio-Visual Target Speech Extraction
Audio-visual target speaker extraction (AV-TSE) models primarily rely on target visual cues to isolate the target speaker's voice from others. We know that humans leverage linguistic knowledge, such as syntax and semantics, to support speech perception. Inspired by this, we explore the potential of pre-trained speech-language models (PSLMs) and pre-trained language models (PLMs) as auxiliary knowledge sources for AV-TSE. In this study, we propose incorporating the linguistic constraints from PS…

Memo: Google offers voluntary buyouts to US staff in the knowledge and information organization, which includes core search and much of the ads organization (The Information)
https://www.theinformation.com/articles/google-offers-buyouts-empl…

Google Offers Buyouts to Employees in Search and Ads Unit
Google is offering voluntary buyouts to employees in the knowledge and information organization, which includes the company’s core search and much of the ads organization, a senior executive told staff on Tuesday. The move comes as Google’s search business faces unprecedented threats from ...

"What Can We Learn From Open Access Journals in the Arab States?" @ Katina Magazine: https://katinamagazine.org/content/article/open-knowledge/2025/open-access-journals-arab-states
"In the 22 Arab states,

An infrared bound on the ultraviolet bounce
Mehrdad Mirbabayi, Giovanni Villadoro
https://arxiv.org/abs/2506.09842 https://arxiv.org/…

An infrared bound on the ultraviolet bounce
Sometimes a local minimum is known to be a metastable vacuum inside the low-energy EFT, but the true vacuum lies outside, and the bounce solution mediating the decay cannot be found. For single-field decay, Espinosa has proposed a family of configurations called ``pseudo-bounces'' as a way to constrain the decay rate. They are parametrized by the central field value $Φ_0$ and constructed without the knowledge of the true vacuum. We prove that the pseudo-bounce family has a monotonically decrea…

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Nanda Rani, Sandeep Kumar Shukla
https://arxiv.org/abs/2506.10175

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Effective attribution of Advanced Persistent Threats (APTs) increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA (Attribution Using Retrieval-Augmented Agents), a multi-agent, knowledge-enhanced framework for automated and interpretable APT attribution. AURA ingests diverse threat data including Tactics, Techniques, and Procedures (TTPs), Indicators of Compromise (IoCs), malware details, adversarial to…

"Knowledge in the Age of AI" - recording of a talk at King's College London
https://youtu.be/2oFrs86xtco?si=NeDgGUepy9gtHDn3

This https://arxiv.org/abs/2505.19111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Remote Sensing Image Classification with Decoupled Knowledge Distillation
To address the challenges posed by the large number of parameters in existing remote sensing image classification models, which hinder deployment on resource-constrained devices, this paper proposes a lightweight classification method based on knowledge distillation. Specifically, G-GhostNet is adopted as the backbone network, leveraging feature reuse to reduce redundant parameters and significantly improve inference efficiency. In addition, a decoupled knowledge distillation strategy is employ…

This Week in Sports Trivia: June 12, 2025 https://www.nytimes.com/athletic/6419531/2025/06/12/this-week-in-sports-trivia-june-12-2025/

This https://arxiv.org/abs/2505.01146 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their application in the biomedical domain presents unique challenges, particularly regarding factual accuracy and up-to-date knowledge integration. Retrieval Augmented Generation (RAG) has emerged as a promising solution to address these challenges by combining the generative capabilities of LLMs with external knowledge retrieval. This comprehensive survey ex…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
Moshi Wei, Sparks Li
https://arxiv.org/abs/2506.09335 https://…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The…

This https://arxiv.org/abs/2505.08640 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Exact quantum noise deconvolution with partial knowledge of noise
We introduce a new quantum noise deconvolution technique that does not rely on the complete knowledge of noise and does not require partial noise tomography. In this new method, we construct a set of observables with completely correctable expectation values despite our incomplete knowledge of noise. This task is achieved just by classical post-processing without extra quantum resources. We show that the number of parameters in the subset of observables with correctable expectation values is th…

Towards a Knowledge Base of Common Sustainability Weaknesses in Green Software Development
Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. Burden
https://arxiv.org/abs/2506.08812

Towards a Knowledge Base of Common Sustainability Weaknesses in Green Software Development
With the climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more resilient digital ecosystem. For developers, getting access to automated tools that analyze code and suggest sustainabilityrelated optimizations becomes extremely important from a learning and implementation perspective. However, there is currently a dearth of such tools due to the lack of standardized knowledge, whi…

This https://arxiv.org/abs/2406.06095 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMS_…

An extension of C++ with memory-centric specifications for HPC to reduce memory footprints and streamline MPI development
The C++ programming language and its cousins lean towards a memory-inefficient storage of structs: The compiler inserts helper bits such that individual instance variables fit to byte or cache boundaries, while it is not able to exploit knowledge about the range of integers, enums or bitsets. Furthermore, the language provides neither support for data exchange via MPI nor for arbitrary floating-point precisions. We propose C++ attributes through which developers can guide the compiler what memo…

Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Shuo Jiang, Min Xie, Frank Youhua Chen, Jian Ma, Jianxi Luo
https://arxiv.org/abs/2506.09755

Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
Research and practice in Intelligent Design (ID) have significantly enhanced engineering innovation, efficiency, quality, and productivity over recent decades, fundamentally reshaping how engineering designers think, behave, and interact with design processes. The recent emergence of Foundation Models (FMs), particularly Large Language Models (LLMs), has demonstrated general knowledge-based reasoning capabilities, and open new paths and avenues for further transformation in engineering design. …

Good Dad knowledge here https://flipboard.social/@ScienceDesk/114664800381751900

A Survey of Link Prediction in N-ary Knowledge Graphs
Jiyao Wei, Saiping Guan, Da Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
https://arxiv.org/abs/2506.08970

A Survey of Link Prediction in N-ary Knowledge Graphs
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significan…

Can knowledge reclassification accelerate technological innovation?
Peter Persoon
https://arxiv.org/abs/2506.08656 https://arxiv.org/…

Can knowledge reclassification accelerate technological innovation?
Technological knowledge evolves not only through the generation of new ideas, but also through the reinterpretation of existing ones. Reinterpretations lead to changes in the classification of knowledge, that is, reclassification. This study investigates how reclassified inventions can serve as renewed sources of innovation, thereby accelerating technological progress. Drawing on patent data as a proxy for technological knowledge, I discuss two empirical patterns: (i) more recent patents are mo…

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
Kiran Sharmaa, Parul Khurana, Ziya Uddina
https://arxiv.org/abs/2506.09056 https://

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
The exponential increase in academic publications has made it increasingly difficult for researchers to remain up to date and systematically synthesize knowledge scattered across vast and fragmented research domains. Literature reviews, particularly those supported by bibliometric methods, have become essential in organizing prior findings and guiding future research directions. While numerous tools exist for bibliometric analysis and network science, there is currently no single platform that …

Slowly oscillating periodic solutions in a nonlinear Volterra equation with non-symmetric feedback
Quentin Griette, Franco Herrera
https://arxiv.org/abs/2506.09564

Slowly oscillating periodic solutions in a nonlinear Volterra equation with non-symmetric feedback
In this work we study a nonlinear Volterra equation with non-symmetric feedback that arises as a particular case of the Gurtin-MacCamy model in population dynamics. We are particularly interested in the existence of slowly oscillating periodic solutions when the trivial stationary state is unstable. Here the absence of symmetry of the nonlinearity prevents the use of many traditional strategies to obtain a priori estimates on the solution. Without a precise knowledge of the period of the soluti…

Distillation of atomistic foundation models across architectures and chemical domains
John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud, Zo\'e Faure Beaulieu, Veronika Juraskova, Laura-Bianca Pa\c{s}ca, Louise A. M. Rosset, Fernanda Duarte, Fausto Martelli, Chris J. Pickard, Volker L. Deringer
https://arxiv.or…

Distillation of atomistic foundation models across architectures and chemical domains
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many different chemical elements and domains, these potentials are widely applicable, but comparably slow and resource-intensive to run. Here we show how distillation via synthetic data can be used to cheaply transfer knowledge from atomistic foundation models to a range of different architectures, unlocking mu…

SoK: Machine Unlearning for Large Language Models
Jie Ren, Yue Xing, Yingqian Cui, Charu C. Aggarwal, Hui Liu
https://arxiv.org/abs/2506.09227 https://

SoK: Machine Unlearning for Large Language Models
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and re-steering hidden representations. While existing surveys often organize these methods by their technical characteristics, such classifications tend to overlook a more fundamental dimension: the underlying…

This https://arxiv.org/abs/2405.12897 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

From Single Genes to Populations: Quantifying Broken Detailed Balance in Transcription
Are there thermodynamic or information theoretic constraints governing mRNA expression? Here we use the framework of stochastic thermodynamics to assess detailed balance breaking in transcription. For the canonical two-state model of transcription, we derive exact analytic expressions for the entropy production rate of transcription at steady state, expressions that can be evaluated from knowledge of the kinetic parameters of the two-state model. This allows us to easily evaluate the entropy pr…

Fuzzy Lattice-based Description Logic
Yiwen Ding, Krishna Manoorkar
https://arxiv.org/abs/2506.05833 https://arxiv.org/pdf/2506.05833…

Fuzzy Lattice-based Description Logic
Recently, description logic LE-ALC was introduced for reasoning in the semantic environment of enriched formal contexts, and a polynomial-time tableaux algorithm was developed to check the consistency of knowledge bases with acyclic TBoxes. In this work, we introduce a fuzzy generalization of LE-ALC called LE-FALC which provides a description logic counterpart of many-valued normal non-distributive logic a.k.a. many-valued LE-logic. This description logic can be used to represent and reason a…

Recently I had a flashback to me telling a friend that I was about to start learning a new concept in programming. Classes. I remember feeling nervous because I felt it was this huge complicated topic, and I already felt my brain was 'full' so-to-speak with programming knowledge. Well that was years ago and now I can't imagine not using classes. It's amazing to look back and see progress! #coding

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new/
[1/1]:
Milgram's experiment in the knowledge space: Individual navigation strategies

A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks
Junhua Lin (University of Edinburgh), Marc Juarez (University of Edinburgh)
https://arxiv.org/abs/2506.10502

A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks
We present a novel attack specifically designed against Tree-Ring, a watermarking technique for diffusion models known for its high imperceptibility and robustness against removal attacks. Unlike previous removal attacks, which rely on strong assumptions about attacker capabilities, our attack only requires access to the variational autoencoder that was used to train the target diffusion model, a component that is often publicly available. By leveraging this variational autoencoder, the attacke…

Catalog of very-high-energy emitting active galactic nuclei at high Galactic latitudes
A. Neronov, D. Semikoz
https://arxiv.org/abs/2506.08497 https://

Catalog of very-high-energy emitting active galactic nuclei at high Galactic latitudes
Large number of Active Galactic Nuclei (AGN) producing Very-High-Energy (VHE) gamma-rays (energies above 100~GeV) has been revealed using observations with Imaging Atmospheric Cherenkov Telescopes (IACTs). However, our knowledge of the VHE emitting AGN population is limited in the absence of an unbiased sky survey. We use long exposure of Fermi Large Area Telescope (LAT) to perform a survey of VHE emitting AGN at Galactic latitudes |b|>10 degrees. We consider clustering of gamma-ray e…

This https://arxiv.org/abs/2412.07184 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Automatic Doubly Robust Forests
This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional nuisance functions. DRRF extends the automatic debiasing framework based on the Riesz representer to the conditional setting and enables nonparametric, forest-based estimation (Athey et al., 2019; Oprescu et al., 2019). In contrast to existing methods, DRRF does not require prior knowledge of the form of the debiasin…

Probing anyon statistics on a single-edge loop in the fractional quantum Hall regime
Flavio Ronetti, No\'e Demazure, J\'er\^ome Rech, Thibaut Jonckheere, Beno\^it Gr\'emaud, Laurent Raymond, Masayuki Hashisaka, Takeo Kato, Thierry Martin
https://arxiv.org/abs/2506.09774

Probing anyon statistics on a single-edge loop in the fractional quantum Hall regime
We propose a setup to directly measure the anyonic statistical angle on a single edge of a fractional quantum Hall system, without requiring independent knowledge of non-universal parameters. We consider a Laughlin edge state bent into a closed loop geometry, where tunneling processes are controllably induced between the endpoints of the loop. To illustrate the underlying physical mechanism, we compute the time-dependent current generated by the injection of multiple anyons, and show that its b…

Movie Wars: How Hollywood and the Media Limit What Films We Can See - Jonathan Rosenbaum
> Ebert reviews a good many film books, and to my knowledge Siskel never did; if he ever read any books about film on his own, it would have surprised me.
#films #movies

History Extra podcast: The Scottish Enlightenment: everything you wanted to know
Episode webpage: http://www.historyextra.com/
Media file:

This https://arxiv.org/abs/2506.06091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MIRIAD: Augmenting LLMs with millions of medical query-response pairs
LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs in high-quality medical knowledge, LLMs have been equipped with external knowledge via RAG, where unstructured medical knowledge is split into small text chunks that can be selectively retrieved and integrated into the LLMs context. Yet, existing RAG pipelines rely on raw, unstructured medical text, which can be noisy,…

Two-site entanglement in the two-dimensional Hubbard model
Frederic Bippus, Anna Kauch, Gerg\H{o} Ro\'osz, Christian Mayrhofer, Fakher Assaad, Karsten Held
https://arxiv.org/abs/2506.09780

Two-site entanglement in the two-dimensional Hubbard model
The study of entanglement in strongly correlated electron systems typically requires knowledge of the reduced density matrix. Here, we apply the parquet dynamical vertex approximation to study the two-site reduced density matrix at varying distance, in the Hubbard model at weak coupling. This allows us to investigate the spatial structure of entanglement in dependence of interaction strength, electron filling, and temperature. We compare results from different entanglement measures, and benchma…

This https://arxiv.org/abs/2506.01644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Budgeted Multi-Level Monte Carlo Method for Full Field Estimates of Multi-PDE Problems
We present a high-performance budgeted multi-level Monte Carlo method for estimates on the entire spatial domain of multi-PDE problems with random input data. The method is designed to operate optimally within memory and CPU-time constraints and eliminates the need for a priori knowledge of the problem's regularity and the algorithm's potential memory demand. To achieve this, we build on the budgeted multi-level Monte Carlo framework and enhance it with a sparse multi-index update algorithm ope…

"Patricia #Espenak announced in an email that Fred passed away yesterday afternoon," Michael Zeiler just wrote on the Solar Eclipse Mailing List: "Fred was in hospice at their home in Portal, Arizona. Pat was holding his hand when he passed away and is devastated.
Fred had an extremely consequential life and was a giant in the solar eclipse community. Let‘s remember his enthusiasm for nature‘s greatest spectacle, his warm friendship to many of us, his generosity in sharing his knowledge, and his impact on all of us."
Obituaries: https://skyandtelescope.org/astronomy-news/fred-espenak-1953-2025/ and https://www.astronomy.com/observing/fred-espenak-dies-at-71/

Zambia's pre-colonial writing system invented by women but almost killed by colonialism - BBC News
https://www.bbc.co.uk/news/articles/c5ye50xgw8vo

Zambia's lost language invented by women but almost killed by colonialism
How social media is pushing back against the still common idea that African societies never had a knowledge system.

This https://arxiv.org/abs/2407.05345 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Review of Non-Equilibrium Thermodynamics And Statistical Mechanics of Vortex Gases in Tornado Theory
This work puts into mathematical, statistical mechanical, and thermodynamical context the initial stages of the genesis of tornado-like vortices with the aim to be consistent with the current state of knowledge of the process of tornadogenesis. In particular, it discusses a mathematical foundation of the formation of coherent structures such as ``cusps'' and ``hairpins'' using variants of the nonlinear Schrödinger equation that arise via the Hasimoto transform of a vortex filament model. The b…

In his keynote, Raphael Troncy is asking whether we should keep building knowledge graphs....taking into account 20 years of experience in building knowledge graphs
https://2025.eswc-conferences.org/keynotes-announcement-raphael-troncy/

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

Refining ensemble $N$-representability of one-body density matrices from partial information
Julia Liebert, Anna O. Schouten, Irma Avdic, Christian Schilling, David A. Mazziotti
https://arxiv.org/abs/2506.09960

Refining ensemble $N$-representability of one-body density matrices from partial information
The $N$-representability problem places fundamental constraints on reduced density matrices (RDMs) that originate from physical many-fermion quantum states. Motivated by recent developments in functional theories, we introduce a hierarchy of ensemble one-body $N$-representability problems that incorporate partial knowledge of the one-body reduced density matrices (1RDMs) within an ensemble of $N$-fermion states with fixed weights $w_i$. Specifically, we propose a systematic relaxation that redu…

Learning event-triggered controllers for linear parameter-varying systems from data
Renjie Ma, Su Zhang, Wenjie Liu, Zhijian Hu, Peng Shi
https://arxiv.org/abs/2506.08366

Learning event-triggered controllers for linear parameter-varying systems from data
Nonlinear dynamical behaviours in engineering applications can be approximated by linear-parameter varying (LPV) representations, but obtaining precise model knowledge to develop a control algorithm is difficult in practice. In this paper, we develop the data-driven control strategies for event-triggered LPV systems with stability verifications. First, we provide the theoretical analysis of $θ$-persistence of excitation for LPV systems, which leads to the feasible data-based representations. T…

"How do you even know that stuff?": Barriers to expertise sharing among spreadsheet users
Qing (Nancy), Xia, Advait Sarkar, Duncan Brumby, Anna Cox
https://arxiv.org/abs/2506.09216

"How do you even know that stuff?": Barriers to expertise sharing among spreadsheet users
Spreadsheet collaboration provides valuable opportunities for learning and expertise sharing between colleagues. Sharing expertise is essential for the retention of important technical skillsets within organisations, but previous studies suggest that spreadsheet experts often fail to disseminate their knowledge to others. We suggest that social norms and beliefs surrounding the value of spreadsheet use significantly influence user engagement in sharing behaviours. To explore this, we conducted …

This https://arxiv.org/abs/2505.21904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into compact experts using limited labeled and abundant unlabeled data. CAST unfolds in three stages: (1) domain adaptation of the VFM teacher(s) via self-training with contrastive pixel calibration, (2) distillation into a compact student via a unified multi-objective loss that couples…

Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
Michael F\"arber, David Lamprecht, Yuni Susanti
https://arxiv.org/abs/2506.08743

Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
Graph Neural Networks (GNNs) have substantially advanced the field of recommender systems. However, despite the creation of more than a thousand knowledge graphs (KGs) under the W3C standard RDF, their rich semantic information has not yet been fully leveraged in GNN-based recommender systems. To address this gap, we propose a comprehensive integration of RDF KGs with GNNs that utilizes both the topological information from RDF object properties and the content information from RDF datatype pro…

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Felix H\"arer
https://arxiv.org/abs/2506.10467 htt…

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Recent advancements in LLMs indicate potential for novel applications, e.g., through reasoning capabilities in the latest OpenAI and DeepSeek models. For applying these models in specific domains beyond text generation, LLM-based multi-agent approaches can be utilized that solve complex tasks by combining reasoning techniques, code generation, and software execution. Applications might utilize these capabilities and the knowledge of specialized LLM agents. However, while many evaluations are pe…

End-to-End Dynamic Metasurface Antenna Wireless System: Prototype, Opportunities, and Challenges
Fran\c{c}ois Yven, Jean Tapie, J\'er\^ome Sol, Philipp del Hougne
https://arxiv.org/abs/2506.09732

End-to-End Dynamic Metasurface Antenna Wireless System: Prototype, Opportunities, and Challenges
Dynamic metasurface antennas (DMAs) are a promising hybrid analog/digital beamforming technology to realize next-generation wireless systems with low cost, footprint, and power consumption. The research on DMA-empowered wireless systems is still at an early stage, mostly limited to theoretical studies under simplifying assumptions on the one hand and a few antenna-level experiments on the other hand. Substantial knowledge gaps arise from the lack of complete end-to-end DMA-empowered wireless sy…

OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary
Yui Sudo, Yusuke Fujita, Atsushi Kojima, Tomoya Mizumoto, Lianbo Liu
https://arxiv.org/abs/2506.09448

OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary
Speech foundation models (SFMs), such as Open Whisper-Style Speech Models (OWSM), are trained on massive datasets to achieve accurate automatic speech recognition. However, even SFMs struggle to accurately recognize rare and unseen words. While contextual biasing (CB) is a promising approach to improve recognition of such words, most CB methods are trained from scratch, resulting in lower performance than SFMs due to the lack of pre-trained knowledge. This paper integrates an existing CB method…

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new/
[1/1]:
Forecasting high-impact research topics via machine learning on evolving knowledge graphs

This https://arxiv.org/abs/2506.03100 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds
Retrieval-augmented generation (RAG) has seen many empirical successes in recent years by aiding the LLM with external knowledge. However, its theoretical aspect has remained mostly unexplored. In this paper, we propose the first finite-sample generalization bound for RAG in in-context linear regression and derive an exact bias-variance tradeoff. Our framework views the retrieved texts as query-dependent noisy in-context examples and recovers the classical in-context learning (ICL) and standard…

The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students
Siddharth Siddharth, Brainerd Prince, Amol Harsh, Shreyas Ramachandran
https://arxiv.org/abs/2506.08041

The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students
This work presents a novel course titled The World of AI designed for first-year undergraduate engineering students with little to no prior exposure to AI. The central problem addressed by this course is that engineering students often lack foundational knowledge of AI and its broader societal implications at the outset of their academic journeys. We believe the way to address this gap is to design and deliver an interdisciplinary course that can a) be accessed by first-year undergraduate engin…

This https://arxiv.org/abs/2505.11386 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory
This paper introduces MutualNeRF, a framework enhancing Neural Radiance Field (NeRF) performance under limited samples using Mutual Information Theory. While NeRF excels in 3D scene synthesis, challenges arise with limited data and existing methods that aim to introduce prior knowledge lack theoretical support in a unified framework. We introduce a simple but theoretically robust concept, Mutual Information, as a metric to uniformly measure the correlation between images, considering both macro…

This https://arxiv.org/abs/2505.19914 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Large Language Models (LLMs), such as OpenAI's o1 and DeepSeek's R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce Enigmata, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable diffic…

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
Yuanhaur Chang, Oren Heller, Yaniv Shlomo, Iddo Bar-Noy, Ella Bokobza, Michal Grinstein-Weiss, Ning Zhang
https://arxiv.org/abs/2506.10025

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
Key decision-makers in small and medium businesses (SMBs) often lack the awareness and knowledge to implement cybersecurity measures effectively. To gain a deeper understanding of how SMB executives navigate cybersecurity decision-making, we deployed a mixed-method approach, conducting semi-structured interviews (n=21) and online surveys (n=322) with SMB key decision-makers. Using thematic analysis, we revealed SMB decision-makers' perceived risks in terms of the digital assets they valued, and…

This https://arxiv.org/abs/2410.16860 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Typical Quantum States of the Universe are Observationally Indistinguishable
We establish three new impossibility results regarding our knowledge of the quantum state of the universe -- a central object in quantum theory. We show that, if the universal quantum state is a typical unit vector from a high-dimensional subspace H_0 of Hilbert space H (such as the one defined by a low-entropy macro-state as prescribed by the Past Hypothesis), then no observation can determine or just significantly narrow down which vector it is. In other words, the overwhelming majority of po…

This https://arxiv.org/abs/2504.20118 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
Traditional Chinese Medicine (TCM) represents a rich repository of ancient medical knowledge that continues to play an important role in modern healthcare. Due to the complexity and breadth of the TCM literature, the integration of AI technologies is critical for its modernization and broader accessibility. However, this integration poses considerable challenges, including the interpretation of obscure classical Chinese texts and the modeling of intricate semantic relationships among TCM concep…

SoK: Evaluating Jailbreak Guardrails for Large Language Models
Xunguang Wang, Zhenlan Ji, Wenxuan Wang, Zongjie Li, Daoyuan Wu, Shuai Wang
https://arxiv.org/abs/2506.10597

SoK: Evaluating Jailbreak Guardrails for Large Language Models
Large Language Models (LLMs) have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety mechanisms. Guardrails--external defense mechanisms that monitor and control LLM interaction--have emerged as a promising solution. However, the current landscape of LLM guardrails is fragmented, lacking a unified taxonomy and comprehensive evaluation framework. In this Systematization of Knowledge (SoK) paper, we pre…

Linking Data Citation to Repository Visibility: An Empirical Study
Fakhri Momeni, Janete Saldanha Bach, Brigitte Mathiak, Peter Mutschke
https://arxiv.org/abs/2506.09530

Linking Data Citation to Repository Visibility: An Empirical Study
In today's data-driven research landscape, dataset visibility and accessibility play a crucial role in advancing scientific knowledge. At the same time, data citation is essential for maintaining academic integrity, acknowledging contributions, validating research outcomes, and fostering scientific reproducibility. As a critical link, it connects scholarly publications with the datasets that drive scientific progress. This study investigates whether repository visibility influences data citatio…

This https://arxiv.org/abs/2505.18700 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains
Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mechanisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo …

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
Eunhye Grace Ko, Shaini Nanayakkara, Earl W. Huff Jr
https://arxiv.org/abs/2506.06225

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
As generative AI (GenAI) becomes increasingly widespread, it is crucial to equip users, particularly vulnerable populations such as older adults (65 and older), with the knowledge to understand its benefits and potential risks. Older adults often exhibit greater reservations about adopting emerging technologies and require tailored literacy support. Using a mixed methods approach, this study examines strategies for delivering GenAI literacy to older adults through a chatbot named Litti, evaluat…

This https://arxiv.org/abs/2506.06197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Metainformation in Quantum Guessing Games
Quantum guessing games offer a structured approach to analyzing quantum information processing, where information is encoded in quantum states and extracted through measurement. An additional aspect of this framework is the influence of partial knowledge about the input on the optimal measurement strategies. This kind of side information can significantly influence the guessing strategy and earlier work has shown that the timing of such side information, whether revealed before or after the mea…

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
Vahid Azizi, Fatemeh Koochaki
https://arxiv.org/abs/2506.07449

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking
Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG approaches predominantly rely on flat, similarity-based retrieval that fails to leverage the rich relational structure inherent in user-item interactions. We introduce LlamaRec-LKG-RAG, a novel single-pass, end-to-end trainable framework that integrates personalized knowledge graph context into LLM-based recommendation r…

Dual Approach to Inverse Covariance Intersection Fusion
Ji\v{r}\'i Ajgl, Ond\v{r}ej Straka
https://arxiv.org/abs/2506.05955 https://

Dual Approach to Inverse Covariance Intersection Fusion
Linear fusion of estimates under the condition of no knowledge of correlation of estimation errors has reached maturity. On the other hand, various cases of partial knowledge are still active research areas. A frequent motivation is to deal with "common information" or "common noise", whatever it means. A fusion rule for a strict meaning of the former expression has already been elaborated. Despite the dual relationship, a strict meaning of the latter one has not been considered so far. The pap…

This https://arxiv.org/abs/2505.16809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some modalities may be missing due to the sequential nature of MRI acquisition, leading to performance degradation. Furthermore, retraining models to accommodate newly available modalities can be inefficient and may cause overfitting, potentially compromising previously learned knowledge. To address these challenges, we p…

From Developer Pairs to AI Copilots: A Comparative Study on Knowledge Transfer
Alisa Welter, Niklas Schneider, Tobias Dick, Kallistos Weis, Christof Tinnes, Marvin Wyrich, Sven Apel
https://arxiv.org/abs/2506.04785

From Developer Pairs to AI Copilots: A Comparative Study on Knowledge Transfer
Knowledge transfer is fundamental to human collaboration and is therefore common in software engineering. Pair programming is a prominent instance. With the rise of AI coding assistants, developers now not only work with human partners but also, as some claim, with AI pair programmers. Although studies confirm knowledge transfer during human pair programming, its effectiveness with AI coding assistants remains uncertain. To analyze knowledge transfer in both human-human and human-AI settings, w…

This https://arxiv.org/abs/2505.16457 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Maximum Separation of Quantum Communication Complexity With and Without Shared Entanglement
We present relation problems whose input size is $n$ such that they can be solved with no communication for entanglement-assisted quantum communication models, but require $Ω(n)$ qubit communication for $2$-way quantum communication models without prior shared entanglement. This is the maximum separation of quantum communication complexity with and without shared entanglement. To our knowledge, our result even shows the first lower bound on quantum communication complexity without shared entan…

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new/
[1/1]:
Human-In-The-Loop Workflow for Neuro- Symbolic Scholarly Knowledge Organization

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models
Wentao Shi, Yiqing Shen
https://arxiv.org/abs/2506.08352

Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models
Large language models (LLMs) can face factual limitations when responding to time-sensitive queries about recent events that arise after their knowledge thresholds in the training corpus. Existing search-augmented approaches fall into two categories, each with distinct limitations: multi-agent search frameworks incur substantial computational overhead by separating search planning and response synthesis across multiple LLMs, while single-LLM tool-calling methods restrict themselves to sequentia…

Spectral Derivatives
Pavel Komarov
https://arxiv.org/abs/2506.06210 https://arxiv.org/pdf/2506.06210…

Spectral Derivatives
One of the happiest accidents in all math is the ease of transforming a function to and taking derivatives in the Fourier frequency domain. But in order to exploit this extraordinary fact without serious artefacting, and in order to be able to use a computer, we need quite a bit of extra knowledge and care. This document sets out the math behind the spectral-derivatives Python package. I touch on fundamental signal processing and calculus concepts as necessary and build upwards.

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

Robust Spin Polarization by Adiabatic Dynamical Decoupling
Soham Pal, Oliver T. Whaites, Wolfgang Knolle, Tania S. Monteiro, Helena S. Knowles
https://arxiv.org/abs/2506.08571

Robust Spin Polarization by Adiabatic Dynamical Decoupling
High-fidelity multi-qubit initialization is vital for quantum simulation, quantum information processing (QIP), and quantum sensing. In diamond platforms, nuclear spin registers can be initialized through polarization transfer from a nearby electronic spin whose high gyromagnetic ratio enables efficient dynamical nuclear polarization (DNP). These hybrid systems are typically controlled using diabatic spin rotations, which require precise knowledge of all system parameters. Adiabatic DNP protoco…

Empirical Quantification of Spurious Correlations in Malware Detection
Bianca Perasso, Ludovico Lozza, Andrea Ponte, Luca Demetrio, Luca Oneto, Fabio Roli
https://arxiv.org/abs/2506.09662

Empirical Quantification of Spurious Correlations in Malware Detection
End-to-end deep learning exhibits unmatched performance for detecting malware, but such an achievement is reached by exploiting spurious correlations -- features with high relevance at inference time, but known to be useless through domain knowledge. While previous work highlighted that deep networks mainly focus on metadata, none investigated the phenomenon further, without quantifying their impact on the decision. In this work, we deepen our understanding of how spurious correlation affects d…

Influential scientists shape knowledge flows between science and IGO policy
Kimitaka Asatani, Yurie Iwata, Yuta Tomokiyo, Basil Mahfouz, Masaru Yarime, Ichiro Sakata
https://arxiv.org/abs/2506.06753

Influential scientists shape knowledge flows between science and IGO policy
Intergovernmental organizations (IGOs) increasingly rely on scientific evidence, yet the pathways through which scientific research enters policy remain opaque. By linking 230,737 scientific papers cited in IGO policy documents (2015-2023) to their authors and collaboration networks, we identify a small group of policy-influential scientists (PI-Sci) who dominate this knowledge flow. These scientists form tightly interconnected, internationally spanning co-authorship networks and achieve policy…

This https://arxiv.org/abs/2504.04178 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundamental gap between the mechanism of LLMs and the requirement of RS, researchers have focused on fine-tuning LLMs with recommendation-specific data to enhance their performance. Language Modeling Loss (LML), originally designed for language generation tasks, is commonly adopted. However, we identify two critical limitation…

This https://arxiv.org/abs/2504.05307 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization
Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata and to ensure compliance with established standards. A CEDAR template specifies the expected fields of a metadata submission and their permissible values. Our standardization process involves u…

Metainformation in Quantum Guessing Games
Teiko Heinosaari, Hanwool Lee
https://arxiv.org/abs/2506.06197 https://arxiv.org/pdf/2506.0…

Metainformation in Quantum Guessing Games
Quantum guessing games offer a structured approach to analyzing quantum information processing, where information is encoded in quantum states and extracted through measurement. An additional aspect of this framework is the influence of partial knowledge about the input on the optimal measurement strategies. This kind of side information can significantly influence the guessing strategy and earlier work has shown that the timing of such side information, whether revealed before or after the mea…

This https://arxiv.org/abs/2408.08208 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy interactions, which significantly impairs recommendation performance. Accurately identifying such noisy interactions without additional information is particularly challenging due to the absence of explicit supervisory signals indicating noise. Large Language Models (LLMs), equipped with extensive open knowledge and se…

This https://arxiv.org/abs/2502.04951 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search
Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by integrating external databases with pre-existing knowledge. However, we observe that these AIPSEs raise risks such as quoting malicious content or citing malicious websites, leading to harmful or unverified information dissemination. In this study, we conduct the first safety risk quantification on seven production AI…

This https://arxiv.org/abs/2401.16185 has been replaced.
link: https://scholar.google.com/scholar?q=a

LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
Large language models (LLMs) have demonstrated significant potential in various tasks, including those requiring human-level intelligence, such as vulnerability detection. However, recent efforts to use LLMs for vulnerability detection remain preliminary, as they lack a deep understanding of whether a subject LLM's vulnerability reasoning capability stems from the model itself or from external aids such as knowledge retrieval and tooling support. In this paper, we aim to decouple LLMs' vulner…

Serendipitous Recommendation with Multimodal LLM
Haoting Wang, Jianling Wang, Hao Li, Fangjun Yi, Mengyu Fu, Youwei Zhang, Yifan Liu, Liang Liu, Minmin Chen, Ed H. Chi, Lichan Hong, Haokai Lu
https://arxiv.org/abs/2506.08283

Serendipitous Recommendation with Multimodal LLM
Conventional recommendation systems succeed in identifying relevant content but often fail to provide users with surprising or novel items. Multimodal Large Language Models (MLLMs) possess the world knowledge and multimodal understanding needed for serendipity, but their integration into billion-item-scale platforms presents significant challenges. In this paper, we propose a novel hierarchical framework where fine-tuned MLLMs provide high-level guidance to conventional recommendation models, s…

Research Knowledge Graphs: the Shifting Paradigm of Scholarly Information Representation
Matth\"aus Zloch, Danilo Dess\`i, Jennifer D'Souza, Leyla Jael Castro, Benjamin Zapilko, Saurav Karmakar, Brigitte Mathiak, Markus Stocker, Wolfgang Otto, S\"oren Auer, Stefan Dietze
https://arxiv.org/abs/2506.07285

Research Knowledge Graphs: the Shifting Paradigm of Scholarly Information Representation
Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured publications pose crucial challenges. Reproducibility of research and finding state-of-the-art methods or data have become increasingly challenging. In this context, the concept of Research Knowledge Graphs (RKGs) has emerged, aiming at providing an easy to u…

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Haowei Wang, Rupeng Zhang, Junjie Wang, Mingyang Li, Yuekai Huang, Dandan Wang, Qing Wang
https://arxiv.org/abs/2506.06151

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on external knowledge makes RAG systems vulnerable to corpus poisoning attacks that manipulate generated outputs via poisoned document injection. Existing poisoning attack strategies typically treat the ret…